Fix render README action

.github/workflows/render-readme.yaml

@@ -1,30 +1,38 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 on:
   push:
-    paths:
-      - README.Rmd
+    paths: ['README.Rmd']
 
-name: Render README
+name: render-readme
 
 jobs:
-  render:
-    name: Render README
-    runs-on: macOS-latest
+  render-readme:
+    runs-on: macos-latest
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
     steps:
-      - uses: actions/checkout@v2
-      - uses: r-lib/actions/setup-r@v1
-      - uses: r-lib/actions/setup-pandoc@v1
-      - name: Install r2dii.data, rmarkdown, remotes, and the local package
+      - name: Checkout repo
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+
+      - name: Install the local package
         run: |
-          install.packages("r2dii.data")
-          install.packages("remotes")
-          remotes::install_local(".")
-          remotes::install_cran("rmarkdown")
-        shell: Rscript {0}
-      - name: Render README
-        run: Rscript -e 'rmarkdown::render("README.Rmd")'
-      - name: Commit results
+          Rscript -e 'install.packages("remotes")'
+          Rscript -e 'remotes::install_local(".")'
+
+      - name: Render README.Rmd file and Commit Results
         run: |
-          git config --local user.email "[email protected]"
-          git config --local user.name "GitHub Actions"
-          git commit README.md -m 'Re-build README.Rmd' || echo "No changes to commit"
+          README_PATH=($(git diff --name-only ${{ github.event.before }} ${{ github.sha }} | grep 'README.Rmd$'))
+          Rscript -e 'for (f in commandArgs(TRUE)) if (file.exists(f)) rmarkdown::render(f)' ${README_PATH[*]}
+          git config --local user.name "$GITHUB_ACTOR"
+          git config --local user.email "[email protected]"
+          git commit ${README_PATH[*]/.Rmd/.md} -m 'Re-build Rmarkdown files' || echo "No changes to commit"
           git push origin || echo "No changes to commit"

README.md

@@ -58,26 +58,26 @@ and run fuzzy matching against all company names in the `abcd`:
 ``` r
 match_result <- match_name(loanbook_demo, abcd_demo)
 match_result 
-#> # A tibble: 26 × 28
-#>    id_direct_loantaker name_direct_loantaker          id_intermediate_parent_1
-#>    <chr>               <chr>                          <chr>                   
-#>  1 C26                 large oil and gas company four <NA>                    
-#>  2 C26                 large oil and gas company four <NA>                    
-#>  3 C1                  large automotive company five  <NA>                    
-#>  4 C1                  large automotive company five  <NA>                    
-#>  5 C35                 large steel company five       <NA>                    
-#>  6 C35                 large steel company five       <NA>                    
-#>  7 C5                  large automotive company two   <NA>                    
-#>  8 C5                  large automotive company two   <NA>                    
-#>  9 C30                 large power company five       <NA>                    
-#> 10 C30                 large power company five       <NA>                    
-#> # ℹ 16 more rows
-#> # ℹ 25 more variables: name_intermediate_parent_1 <chr>,
+#> # A tibble: 307 × 28
+#>    id_loan id_direct_loantaker name_direct_loantaker      id_intermediate_pare…¹
+#>    <chr>   <chr>               <chr>                      <chr>                 
+#>  1 L1      C294                Vitale Group               <NA>                  
+#>  2 L3      C292                Rowe-Rowe                  IP5                   
+#>  3 L5      C305                Ring AG & Co. KGaA         <NA>                  
+#>  4 L6      C304                Kassulke-Kassulke          <NA>                  
+#>  5 L6      C304                Kassulke-Kassulke          <NA>                  
+#>  6 L7      C227                Morissette Group           <NA>                  
+#>  7 L7      C227                Morissette Group           <NA>                  
+#>  8 L8      C303                Barone s.r.l.              <NA>                  
+#>  9 L9      C301                Werner Werner AG & Co. KG… IP10                  
+#> 10 L9      C301                Werner Werner AG & Co. KG… IP10                  
+#> # ℹ 297 more rows
+#> # ℹ abbreviated name: ¹​id_intermediate_parent_1
+#> # ℹ 24 more variables: name_intermediate_parent_1 <chr>,
 #> #   id_ultimate_parent <chr>, name_ultimate_parent <chr>,
 #> #   loan_size_outstanding <dbl>, loan_size_outstanding_currency <chr>,
 #> #   loan_size_credit_limit <dbl>, loan_size_credit_limit_currency <chr>,
-#> #   sector_classification_system <chr>, sector_classification_input_type <chr>,
-#> #   sector_classification_direct_loantaker <dbl>, fi_type <chr>, …
+#> #   sector_classification_system <chr>, …
 ```
 
 ### 2. Prioritize validated matches
@@ -92,29 +92,26 @@ matches, prioritizing (by default) `direct_loantaker` matches over
 
 ``` r
 prioritize(match_result)
-#> # A tibble: 13 × 28
-#>    id_direct_loantaker name_direct_loantaker          id_intermediate_parent_1
-#>    <chr>               <chr>                          <chr>                   
-#>  1 C26                 large oil and gas company four <NA>                    
-#>  2 C1                  large automotive company five  <NA>                    
-#>  3 C35                 large steel company five       <NA>                    
-#>  4 C5                  large automotive company two   <NA>                    
-#>  5 C30                 large power company five       <NA>                    
-#>  6 C3                  large automotive company one   <NA>                    
-#>  7 C23                 large hdv company three        <NA>                    
-#>  8 C33                 large power company three      <NA>                    
-#>  9 C31                 large power company four       <NA>                    
-#> 10 C32                 large power company one        <NA>                    
-#> 11 C34                 large power company two        <NA>                    
-#> 12 C25                 large oil and gas company five <NA>                    
-#> 13 C20                 large coal company two         <NA>                    
-#> # ℹ 25 more variables: name_intermediate_parent_1 <chr>,
+#> # A tibble: 168 × 28
+#>    id_loan id_direct_loantaker name_direct_loantaker      id_intermediate_pare…¹
+#>    <chr>   <chr>               <chr>                      <chr>                 
+#>  1 L6      C304                Kassulke-Kassulke          <NA>                  
+#>  2 L13     C297                Ladeck                     <NA>                  
+#>  3 L20     C287                Weinhold                   <NA>                  
+#>  4 L21     C286                Gallo Group                <NA>                  
+#>  5 L22     C285                Austermuhle GmbH           <NA>                  
+#>  6 L24     C282                Ferraro-Ferraro Group      <NA>                  
+#>  7 L25     C281                Lockman, Lockman and Lock… <NA>                  
+#>  8 L26     C280                Ankunding, Ankunding and … <NA>                  
+#>  9 L27     C278                Donati-Donati Group        <NA>                  
+#> 10 L28     C276                Ferraro, Ferraro e Ferrar… <NA>                  
+#> # ℹ 158 more rows
+#> # ℹ abbreviated name: ¹​id_intermediate_parent_1
+#> # ℹ 24 more variables: name_intermediate_parent_1 <chr>,
 #> #   id_ultimate_parent <chr>, name_ultimate_parent <chr>,
 #> #   loan_size_outstanding <dbl>, loan_size_outstanding_currency <chr>,
 #> #   loan_size_credit_limit <dbl>, loan_size_credit_limit_currency <chr>,
-#> #   sector_classification_system <chr>, sector_classification_input_type <chr>,
-#> #   sector_classification_direct_loantaker <dbl>, fi_type <chr>,
-#> #   flag_project_finance_loan <chr>, name_project <chr>, …
+#> #   sector_classification_system <chr>, …
 ```
 
 The result is a dataset with identical columns to the input loanbook,